Evaluating LLM Performance: Less is more!
A Comparative Analysis of Anthropic's Claude and OpenAI's GPT Models
Introduction
Welcome to the inaugural report from the Talentpath Research team. Our mission is to support startups and founders by exploring significant topics, uncovering trends, and identifying best practices that save time and resources. In this study, we focus on evaluating the capabilities of two prominent Large Language Models (LLMs): Anthropic's Claude and OpenAI's GPT models.
Methodology
Our research aimed to assess the accuracy and efficiency of these LLMs in handling educational content. We employed a straightforward testing approach using a pre-labeled dataset comprising over 1,000 general multiple-choice science questions intended for students from 3rd to 9th grade.
The evaluation consisted of two primary tests:
Initial Test (Without Context): Both LLMs were provided only with the questions and the multiple-choice answers.
Secondary Test (With Context): Both LLMs received the same questions and answer choices, supplemented by an additional relevant informational blurb to serve as context.
This methodology allowed us to analyze how additional context influences the performance of each model.
Results
Overall Performance
The findings revealed a counterintuitive trend: both models performed better without the added context. Introducing extra information led to approximately a 6% reduction in accuracy for both Claude and the OpenAI model. This suggests that excessive context may overwhelm the models or introduce distractions that hinder their ability to select the correct answer.
Less is more?
When examining the questions both models initially failed without context (26 questions), adding context did not lead to a significant improvement in accuracy. Conversely, for the questions they failed with context (66 questions), removing the extra information resulted in a marked increase in performance. This underscores the potential negative impact of unnecessary context on model accuracy.
Both models showed a significant improvement—70% to 80% better—on questions they initially failed when the context was adjusted appropriately.
Numerical Computation Challenges
An interesting trend was observed in questions involving numerical computations. Both models exhibited a reduction in performance on these types of questions, which was more pronounced when context was provided. Specifically:
Without Context:
Claude: Faced a 2% reduction in accuracy.
OpenAI's Model: Experienced a 9% reduction.
With Context:
Both models showed a 6% to 7% reduction in accuracy.
This indicates that numerical computations are a relative weakness for both models, and additional context may exacerbate the challenge.
Prompt Compliance Issues
We also noted instances where adding context led to previously effective prompts failing to produce compliant results. In some cases, the extra information appeared to cause the models to disregard prior instructions, suggesting that context can interfere with the models' ability to follow directives accurately.
Discussion
The study highlights that providing more information to LLMs does not necessarily enhance their performance. In fact, excessive or irrelevant context can decrease accuracy and lead to non-compliant outputs. The models' struggle with numerical computations, especially when additional context is provided, points to a need for improvement in handling math-related tasks.
These findings have practical implications for how we interact with and utilize LLMs. They suggest that a minimalist approach—supplying only essential information—is more effective than overloading the models with data.
Conclusions
Both Anthropic's Claude and OpenAI's GPT models demonstrate higher accuracy when operating with minimal, relevant information. The introduction of unnecessary context can negatively impact their performance, particularly in tasks involving numerical computations or where strict adherence to prompts is required.
Implications for Users
What this means for you:
Simplicity is Essential: When working with LLMs, provide only the necessary information needed to complete the task. Avoid adding extraneous details that may confuse the model.
Structured Workflow: Break down complex tasks into smaller, manageable steps. This helps the model focus on specific instructions without being sidetracked by irrelevant context.
Be Cautious with Numerical Tasks: Recognize that LLMs may have limitations with math and numeric applications. For tasks requiring precise calculations, consider using specialized tools or verifying the results independently.
Optimize Prompts Carefully: Craft clear and concise prompts. Ensure that instructions are explicit and free from ambiguity to enhance the likelihood of accurate and compliant responses.
Authors
Jean Turban & Bryan Navarro